CN116579880A - Effective green water resource evaluation method and device based on dynamic crop coefficients - Google Patents
Effective green water resource evaluation method and device based on dynamic crop coefficients Download PDFInfo
- Publication number
- CN116579880A CN116579880A CN202310517442.2A CN202310517442A CN116579880A CN 116579880 A CN116579880 A CN 116579880A CN 202310517442 A CN202310517442 A CN 202310517442A CN 116579880 A CN116579880 A CN 116579880A
- Authority
- CN
- China
- Prior art keywords
- vegetation
- evaporation
- crop
- leaf area
- green water
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 163
- 238000011156 evaluation Methods 0.000 title claims abstract description 36
- 238000001704 evaporation Methods 0.000 claims abstract description 111
- 230000008020 evaporation Effects 0.000 claims abstract description 93
- 238000004088 simulation Methods 0.000 claims abstract description 65
- 230000012010 growth Effects 0.000 claims abstract description 53
- 238000000034 method Methods 0.000 claims abstract description 46
- 230000005068 transpiration Effects 0.000 claims abstract description 37
- 238000004364 calculation method Methods 0.000 claims abstract description 31
- 230000014509 gene expression Effects 0.000 claims abstract description 24
- 238000012544 monitoring process Methods 0.000 claims abstract description 17
- 239000002689 soil Substances 0.000 claims description 42
- 230000004044 response Effects 0.000 claims description 18
- 238000009825 accumulation Methods 0.000 claims description 12
- 238000010276 construction Methods 0.000 claims description 9
- 230000009189 diving Effects 0.000 claims description 7
- 238000010521 absorption reaction Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 239000003673 groundwater Substances 0.000 claims description 3
- 230000014759 maintenance of location Effects 0.000 claims description 3
- 230000035515 penetration Effects 0.000 claims description 3
- 238000007740 vapor deposition Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 19
- 241000196324 Embryophyta Species 0.000 description 11
- 238000007726 management method Methods 0.000 description 9
- 241000209094 Oryza Species 0.000 description 8
- 235000007164 Oryza sativa Nutrition 0.000 description 8
- 235000009566 rice Nutrition 0.000 description 8
- 240000000111 Saccharum officinarum Species 0.000 description 6
- 235000007201 Saccharum officinarum Nutrition 0.000 description 6
- 238000003860 storage Methods 0.000 description 5
- 238000012795 verification Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000003973 irrigation Methods 0.000 description 4
- 230000008635 plant growth Effects 0.000 description 4
- 238000001556 precipitation Methods 0.000 description 4
- 238000009331 sowing Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 3
- 238000009355 double cropping Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000002262 irrigation Effects 0.000 description 3
- 239000004576 sand Substances 0.000 description 3
- 241000282414 Homo sapiens Species 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 239000004927 clay Substances 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 241000481604 Bolbostemma Species 0.000 description 1
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 241000282412 Homo Species 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 239000003513 alkali Substances 0.000 description 1
- 230000003698 anagen phase Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000035558 fertility Effects 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 239000002985 plastic film Substances 0.000 description 1
- 229920006255 plastic film Polymers 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 230000002786 root growth Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000010902 straw Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Economics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Primary Health Care (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- General Health & Medical Sciences (AREA)
- Algebra (AREA)
- Public Health (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of water resource evaluation, in particular to an effective green water resource evaluation method and device based on dynamic crop coefficients. Comprising the following steps: constructing an SWAT model of the region to be detected; constructing a mathematical relation expression of potential transpiration of vegetation, and introducing dynamic crop coefficients into the SWAT model; evaluating the result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating crop growth parameters and evapotranspiration parameters; and carrying out evaporation effectiveness judgment according to the land cover type and the dynamic coverage calculation method, and carrying out daily simulation to obtain the total amount of effective green water resources in the to-be-detected area. According to the technical scheme, dynamic crop coefficients are introduced into the model, so that the accuracy of the evapotranspiration simulation and the simulation of the crop growth process is improved, the dynamic calculation of vegetation coverage is performed, the effective green water resource division based on the dynamic coverage calculation is realized, and the accuracy of the effective green water resource evaluation is improved.
Description
Technical Field
The invention relates to the technical field of water resource evaluation, in particular to an effective green water resource evaluation method and device based on dynamic crop coefficients.
Background
Green water is the water consumed by forests, grasslands, wetlands and farmlands through transpiration, is the main water source for supporting land ecosystems and rainy agricultural production, and accounts for 65% of total precipitation on a global scale, while water resources (blue water) in the traditional sense account for only 35%. The water resource evaluation scope is widened, and the green water accounting for 65% of total precipitation is put into the water resource evaluation system, so that the water resource evaluation system is more reasonable and comprehensive, the proper proportion of blue water to green water is maintained, and more effective guidance is provided for more effectively utilizing water resources and scientifically managing the water resources.
Solar radiation, air humidity, temperature and wind speed are important factors affecting the evaporation process. Therefore, green water resource evaluation generally adopts a Penman-Montetith formula with high applicability to calculate the evaporation. This is why distributed hydrologic models mostly use the Penman-Monteth formula to model evaporative emissions. In addition, the distributed hydrologic model not only can fully consider the space variability of various influencing factors, but also can simulate the vegetation growth process, reveal the green water conversion process mechanism, and enable the green water simulation and quantitative division of different time-space scales to be possible. In reality, however, vegetation evapotranspiration may differ from reference evapotranspiration due to factors such as leaf structure, stomatal characteristics, aerodynamic properties, root growth conditions, etc. of different vegetation types. The Penman-Monteth formula only considers the influence of meteorological conditions on crop evaporation, namely reference evaporation, but does not consider the characteristics of specific crops, and cannot accurately reflect the actual evaporation conditions of different vegetation types in respective growth periods, so that the hydrologic model has a defect in green water simulation.
In addition, in the conventional water resource evaluation, green water resources (evapotranspiration) which can be utilized by crops are ignored for blue water resources which can meet the consumption of a social and economic system, and the conventional water resource evaluation cannot completely reflect the whole connotation of the water resources, so that the water resource evaluation caliber lacks layering property, and the huge difference of the water resource evaluation amount is directly caused. Therefore, green water should be included in the water resource evaluation category. The green water is circularly supplied to the terrestrial ecological system to reflect the water consumption of the natural soil-vegetation ecological system, and the water consumption comprises effective green water and ineffective green water. Only the green water resources which are directly utilized for human production activities, directly participate in the production process of economic and ecological quantities or provide effective environmental service for relevant ecological bodies in the circulation process are effective. In addition to this, green water does not produce economic, social value to humans, which is not effective. And identifying effective green water resources which can generate value to the region, so as to really meet the purpose of water resource evaluation.
In general, canopy interception evaporation, surface evaporation and vegetation transpiration are all considered as effective green water because they are involved in the physiological process of plants; soil evaporation in land use types such as sand, gobi, saline-alkali land, swamp land, bare gravel land, sparse grassland, and the like is an ineffective green water. The most critical is the inter-plant evaporation of the plants, which is divided according to vegetation coverage. In the prior art, the effective green water is calculated by multiplying the number of green water by a coverage coefficient of a fixed value according to different growth stages of vegetation. However, the coverage is continuously changed along with the growth of vegetation, and the dividing method based on static coverage easily causes large errors in effective green water division.
Therefore, the existing water resource evaluation method has the defects in green water simulation, and is easy to cause large errors in effective green water division, so that the effective utilization and scientific management of green water resources are not facilitated.
Disclosure of Invention
In view of the above, the present invention aims to provide an effective green water resource evaluation method and apparatus based on dynamic crop coefficients, so as to solve the problems of the prior art that the green water simulation is not enough and a large error is easily caused in the effective green water division.
According to a first aspect of an embodiment of the present invention, there is provided an effective green water resource evaluation method based on dynamic crop coefficients, including:
dividing and extracting the region to be detected to obtain drainage basin information, and dividing a hydrological response unit according to the drainage basin information;
constructing a SWAT model according to the hydrological response unit, the weather data, the reservoir data and the farmland management data which are acquired in advance;
constructing a mathematical relation expression of potential transpiration of vegetation according to the pre-acquired crop information, soil information and meteorological information, and optimizing a vegetation transpiration calculation formula in the SWAT model according to the expression to realize simulation of dynamic crop coefficients;
evaluating the evapotranspiration result and the crop leaf area index simulation result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating crop growth parameters and evapotranspiration parameters in the SWAT model according to the evaluation result;
judging the evaporation effectiveness according to the land cover type and the dynamic coverage calculation method;
and according to the judging result, simulating vegetation coverage day by using the SWAT model, and obtaining the total amount of effective green water resources in the to-be-detected area according to the simulation result.
Preferably, the dividing and extracting the to-be-detected area to obtain the drainage basin information, and obtaining the hydrological response unit according to the drainage basin information includes:
importing grid DEM data of the region to be detected into a geographic information system platform to obtain natural sub-river basin division data and river network water system data as river basin information;
and importing the river basin information into an initial SWAT model, and dividing the hydrological response unit according to the land utilization type, the soil type and the gradient type.
Preferably, the constructing a mathematical relationship expression of the potential transpiration amount of the vegetation according to the pre-acquired crop information, soil information and meteorological information includes:
the mathematical relational expression of the potential transpiration of vegetation is as follows:
wherein EP represents the potential transpiration of vegetation; ET (electric T) 0 Indicating a reference vapor deposition amount; k (K) c ini 、K c mid 、K c end Crop coefficients of vegetation in an initial growth period, a rapid growth period and a late growth period are respectively represented; gluc represents the plant heat accumulation unit percentage; fr gw1 And fr gw2 Respectively representing the heat accumulation ratio corresponding to the first point and the second point on the optimal leaf area index curve.
Preferably, the evaluating the evapotranspiration result and the crop leaf area index simulation result obtained by operating the SWAT model according to the pre-acquired actually measured evaporation data and the leaf area index monitoring data includes:
and selecting a correlation coefficient and a Nash-Sutcliffe efficiency coefficient to evaluate and operate the evaporation result and the crop leaf area index simulation result obtained by the SWAT model based on the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance.
Preferably, the rated crop growth parameters include: the method comprises the steps of determining a temperature accumulation ratio corresponding to a first point on an optimal leaf area index curve, determining a temperature accumulation ratio corresponding to a second point on the optimal leaf area index curve, determining a leaf area ratio corresponding to the first point on the optimal leaf area index curve, determining a leaf area ratio corresponding to the second point on the optimal leaf area index curve, and determining a potential maximum leaf area index and a temperature accumulation ratio corresponding to the beginning of decay of the leaf area index;
the rated evapotranspiration parameters comprise: soil evaporation compensation factors, plant absorption compensation factors, shallow groundwater re-evaporation coefficients, shallow aquifers're-evaporation' or penetration to a threshold depth of a deep aquifer, soil layer effective water capacity and vegetation retention capacity.
Preferably, the determining the evaporation effectiveness according to the land cover type and the dynamic coverage calculating method includes:
the water which participates in vegetation transpiration is judged to be effective green water resources by intercepting and evaporating the canopy, intercepting and evaporating the building in the construction site, evaporating the swamps or water areas and diving and evaporating;
calculating the vegetation coverage of the forestation land and the non-high-density grassland by using a dynamic coverage calculation method;
soil evaporation between vegetation is calculated from the coverage of the forestation, non-high density grassland vegetation.
Preferably, the formula of the dynamic coverage calculation method is as follows:
a i ＝LAI i /LAI mx
wherein a is i Vegetation coverage representing the day Ji Di i of vegetation growth; LAI (LAI) i Leaf area index representing the days Ji Di i of vegetation growth; LAI (LAI) mx Representing the maximum leaf area index during the vegetation period.
Preferably, the step of performing a daily simulation by using the SWAT model, and obtaining the total amount of the effective green water resources in the to-be-detected area according to the simulation result includes:
the total amount of the effective green water resources in the area to be measured is obtained by the following formula:
wherein Wgreen a Representing the total amount of effective green water resources in the area; w (W) can,i Represents the trapped evaporation amount on the i day; wwet i Represents the evaporation capacity of the wetland on the i th day; wwt i Indicating the evaporation capacity of the water area on the ith day; wgw i Represents the diving evaporation amount on the i th day; wep ij Representing the transpiration amount of vegetation on the ith day of the jth vegetation; wes ij The soil evaporation capacity of the j-th vegetation on the i-th day is represented; w (W) un,i Indicating the unutilized land on the i-th daySoil evaporation capacity; a, a ij Representing vegetation coverage of the j-th vegetation on the i-th day; m represents vegetation species in the river basin; n represents the total number of days of the year.
Preferably, the method further comprises:
the total amount of invalid green water resources in the area to be measured is obtained by the following formula:
wherein Wgreen u Indicating the total amount of regional ineffective green water resources; w (W) un,i The evaporation amount of the soil from the unused land on the i-th day is shown.
According to a second aspect of the embodiment of the present invention, there is provided an effective green water resource evaluation device based on dynamic crop coefficients, including:
the model construction module is used for carrying out division and extraction on the region to be detected to obtain drainage basin information, and dividing the hydrological response unit according to the drainage basin information; constructing a SWAT model according to the hydrological response unit, the weather data, the reservoir data and the farmland management data which are acquired in advance;
the optimizing module is used for constructing a mathematical relation expression of potential transpiration of vegetation according to the pre-acquired crop information, soil information and meteorological information, optimizing a vegetation transpiration calculation formula in the SWAT model according to the expression, and realizing simulation of dynamic crop coefficients;
the parameter calibration module is used for evaluating the evapotranspiration result and the crop leaf area index simulation result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating the crop growth parameters and the evapotranspiration parameters in the SWAT model according to the evaluation result;
the judging module is used for judging the evaporation effectiveness according to the land cover type and the dynamic coverage calculation method;
and the result generation module is used for simulating vegetation coverage day by utilizing the SWAT model according to the judging result, and obtaining the total amount of effective green water resources in the to-be-detected area according to the simulation result.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
it can be understood that the technical scheme provided by the invention can construct the SWAT model of the region to be detected; constructing a mathematical relation expression of potential transpiration of vegetation, and introducing dynamic crop coefficients into the SWAT model; evaluating the result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating crop growth parameters and evapotranspiration parameters; and carrying out evaporation effectiveness judgment according to the land cover type and the dynamic coverage calculation method, and carrying out daily simulation to obtain the total amount of effective green water resources in the to-be-detected area. According to the technical scheme provided by the invention, dynamic crop coefficients are introduced, so that the characteristics of specific plant types are fully considered in the model in the evapotranspiration simulation process, the actual evapotranspiration and leaf area index change conditions of different vegetation types in respective growth periods are accurately reflected, and the accuracy of green water simulation and crop growth process simulation is improved; on the basis of improving the simulation accuracy of the crop growth process, a dynamic coverage calculation method is introduced, so that the accuracy of the effective green water resource evaluation is improved, the water resource evaluation caliber is widened, and more effective guidance is provided for more effective utilization and scientific management of water resources.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic step diagram illustrating a method for efficient green water resource assessment based on dynamic crop coefficients, according to an exemplary embodiment;
FIG. 2 is a graph showing simulated values versus measured values of rice and sugarcane leaf area index according to one exemplary embodiment;
FIG. 3 is a graph showing a comparison of measured and simulated month evapotranspiration of Yulin Bolbostemma Rate period and verification period according to an exemplary embodiment;
FIG. 4 is a graph showing a comparison of measured and simulated month transpiration processes for the pump up rate period and verification period, according to an exemplary embodiment;
FIG. 5 is a graphical illustration of watershed effective and ineffective green water resource spatial profiles according to an exemplary embodiment;
fig. 6 is a schematic block diagram illustrating an efficient green water resource assessment device based on dynamic crop coefficients, according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Example 1
Fig. 1 is a schematic step diagram of a method for evaluating an effective green water resource based on a dynamic crop coefficient according to an exemplary embodiment, referring to fig. 1, there is provided a method for evaluating an effective green water resource based on a dynamic crop coefficient, including:
s11, dividing and extracting a region to be detected to obtain drainage basin information, and dividing a hydrological response unit according to the drainage basin information;
step S12, constructing a SWAT model according to the hydrological response unit, the weather data, the reservoir data and the farmland management data which are acquired in advance;
step S13, constructing a mathematical relation expression of potential transpiration of vegetation according to the pre-acquired crop information, soil information and meteorological information, and optimizing a vegetation transpiration calculation formula in the SWAT model according to the expression to realize simulation of dynamic crop coefficients;
s14, evaluating an evapotranspiration result and a crop leaf area index simulation result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating crop growth parameters and evapotranspiration parameters in the SWAT model according to the evaluation result;
and calibrating the crop growth parameters and the evapotranspiration parameters to realize the calibration of the results.
S15, judging evaporation effectiveness according to the land cover type and the dynamic coverage calculation method;
and S16, simulating vegetation coverage day by using the SWAT model according to the judging result, and obtaining the total amount of effective green water resources in the to-be-detected area according to the simulation result.
It can be understood that the technical scheme provided by the embodiment can construct a SWAT model of the region to be detected; constructing a mathematical relation expression of potential transpiration of vegetation, and introducing dynamic crop coefficients into the SWAT model; evaluating the result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating crop growth parameters and evapotranspiration parameters; and carrying out evaporation effectiveness judgment according to the land cover type and the dynamic coverage calculation method, and carrying out daily simulation to obtain the total amount of effective green water resources in the to-be-detected area. According to the technical scheme provided by the embodiment, dynamic crop coefficients are introduced, so that the characteristics of specific plant types are fully considered in the model in the evapotranspiration simulation process, the actual evapotranspiration and leaf area index change conditions of different vegetation types in respective growth periods are accurately reflected, and the accuracy of green water simulation and crop growth process simulation is improved; on the basis of improving the simulation accuracy of the crop growth process, a dynamic coverage calculation method is introduced, so that the accuracy of the effective green water resource evaluation is improved, the water resource evaluation caliber is widened, and more effective guidance is provided for more effective utilization and scientific management of water resources.
In step S11, the dividing and extracting the to-be-detected area to obtain the drainage basin information, and obtaining the hydrological response unit according to the drainage basin information includes:
importing grid DEM data of a region to be detected into a geographic information system platform (ArcGIS) to obtain natural sub-river basin division data and river network water system data as river basin information;
and importing the river basin information into an initial SWAT model, and dividing the hydrological response unit according to the land utilization type, the soil type and the gradient type.
In step S12, reservoir data includes: reservoir capacity, delivery flow, etc.; the farmland management data includes: crop planting type, planting time, harvesting time, etc.
In step S13, a mathematical relationship expression of the potential transpiration amount of vegetation is constructed according to the pre-acquired crop information, soil information and meteorological information, and the method includes:
the mathematical relational expression of the potential transpiration of vegetation is as follows:
wherein EP represents potential transpiration of vegetation, and the unit is mm; ET (electric T) 0 Indicating a reference vapor emission amount in mm; k (K) c ini 、K c mid 、K c end Crop coefficients of vegetation in an initial growth period, a rapid growth period and a late growth period are respectively represented; gluc represents the plant heat accumulation unit percentage; fr gw1 And fr gw2 Respectively representing the heat accumulation ratio corresponding to the first point and the second point on the optimal leaf area index curve.
Specifically, K c ini The calculation formula of (2) is as follows:
wherein t is w Representing the average interval time of irrigation or rainfall; REW represents the amount of water evaporated in mm at the atmospheric evaporation force control stage; TEW represents the total amount of water evaporated in mm after a single rainfall or irrigation.
The expressions of REW and TEW are as follows:
in the above formula, H represents the depth of the soil evaporation layer, and the value is usually 100-150 mm; θ Fc And theta WP Respectively the field water holding capacity and the wilting point water holding capacity of the soil of the evaporation layer; sand and Clay are Sand content and Clay content, respectively, in the soil.
K c mid And K c end The calculation formulas of (a) are respectively as follows:
wherein v is 2 Representing the average wind speed at a height of 2m during the fertility phase; h represents the average height of vegetation in the growth stage; k (K) cm And K cd Crop coefficient reference values respectively representing the mid-stage and late-stage of plant growth provided by FAO; RH (relative humidity) min Representing the average value of the minimum relative humidity during this growth phase.
RH min The specific expression of (2) is as follows:
wherein T is max And T min The daily maximum air temperature and the daily minimum air temperature are respectively indicated.
In step S14, the evaluating the evaporation result and the crop leaf area index simulation result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data obtained in advance includes:
and selecting a correlation coefficient and a Nash-Sutcliffe efficiency coefficient to evaluate and operate the evaporation result and the crop leaf area index simulation result obtained by the SWAT model based on the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance.
In specific practice, the constructed SWAT model is operated, the simulation results of evaporation and crop leaf area indexes are output, the simulation effect is evaluated by selecting a correlation coefficient and a Nash-Sutcliffe efficiency coefficient based on actually measured evaporation and leaf area index monitoring data, and crop growth parameters and evaporation parameters of the model are calibrated.
The rated crop growth parameters include: the temperature ratio (FRGRW 1) corresponding to the first point on the optimal leaf area index curve, the temperature ratio (FRGRW 2) corresponding to the second point on the optimal leaf area index curve, the leaf area ratio (LAIMX 1) corresponding to the first point on the optimal leaf area index curve, the leaf area ratio (LAIMX 2) corresponding to the second point on the optimal leaf area index curve, the potential maximum leaf area index (BLAI) and the temperature ratio (DLAI) corresponding to the onset of decay of the leaf area index;
the rated evapotranspiration parameters comprise: soil evaporation compensation factor (ESCO), plant absorption compensation factor (EPCO), shallow groundwater re-evaporation factor (gw_revap), shallow aquifer "re-evaporation" or penetration to a threshold depth of deep aquifer (revpmn), soil layer effective water capacity (sol_awc), and vegetation retention Capacity (CANMX).
In step S15, the evaporation effectiveness determination according to the land cover type and the dynamic coverage calculation method includes:
the water which participates in vegetation transpiration is judged to be effective green water resources by intercepting and evaporating the canopy, intercepting and evaporating the building in the construction site, evaporating the swamps or water areas and diving and evaporating;
calculating the vegetation coverage of the forestation land and the non-high-density grassland by using a dynamic coverage calculation method;
soil evaporation between vegetation is calculated from the coverage of the forestation, non-high density grassland vegetation.
In specific practice, the effective green water resource evaluation of different land cover types is as follows:
the average storage variable of the soil water storage for many years can be considered as zero, and the green water resource quantity is zero;
the interception evaporation of the canopy and the interception evaporation of the construction site building are beneficial to maintaining a physiological environment suitable for vegetation and a living environment suitable for human beings, and the effective green water resource is judged;
evaporation of the marshes and water areas is beneficial to maintaining the ecological functions of the wetland, and all the evaporation is judged to be an effective green water resource;
the diving evaporation is converted from underground water resources, and is judged to be effective green water resources;
the water which participates in the transpiration of the vegetation directly participates in the growth process of the vegetation, and is judged to be an effective green water resource;
whereas for soil evaporation between crops, forests, etc. (inter-plant evaporation), only a part belongs to the effective green water, the effectiveness of which depends on the vegetation coverage. Generally, if the vegetation coverage is low and far from shielding the space between the vegetation, then the soil evaporation is basically ineffective for vegetation growth, which is an ineffective green water resource. Therefore, evaporation of bare rock soil, sparse grasslands, and other difficult-to-use lands is all defined as ineffective green water resources. For open forest lands and non-high density grasslands, the effective soil evaporation amount needs to be calculated by considering vegetation coverage.
It should be noted that, the formula of the dynamic coverage calculation method is as follows:
a i ＝LAI i /LAI mx
wherein a is i Vegetation coverage representing the day Ji Di i of vegetation growth; LAI (LAI) i Leaf area index representing the days Ji Di i of vegetation growth; LAI (LAI) mx Representing the maximum leaf area index during the vegetation period.
Specifically, for annual or perennial plants in grasslands, shrubs, cultivated lands, the leaf area index is calculated as follows:
wherein fr phu,i A cumulative heat unit score representing the Ji Di i day of plant growth; fr Lm,i The maximum leaf area index fraction for Ji Di i days of plant growth was expressed as follows:
wherein r is 1 And r 2 Representing a first shape factor and a second shape factor, respectively; LAI (LAI) mx1 And LAI mx2 Respectively, the leaf area ratios corresponding to the first point and the second point on the optimal leaf area index curve.
For trees in a woodland, the leaf area index calculation formula is as follows:
wherein yr c And yr f Representing the current age of the tree and the age of the tree, respectively.
It should be noted that, the performing the daily simulation by using the SWAT model, and obtaining the total amount of the effective green water resources in the to-be-detected area according to the simulation result includes:
the total amount of the effective green water resources in the area to be measured is obtained by the following formula:
wherein Wgreen a Representing the total amount of effective green water resources in the area; w (W) can,i Represents the trapped evaporation amount on the i day; wwet i Represents the evaporation capacity of the wetland on the i th day; wwt i Indicating the evaporation capacity of the water area on the ith day; wgw i Represents the diving evaporation amount on the i th day; wep ij Representing the transpiration amount of vegetation on the ith day of the jth vegetation; wes ij The soil evaporation capacity of the j-th vegetation on the i-th day is represented; w (W) un,i Indicating the evaporation amount of the soil of the unused land on the i-th day; a, a ij Representing vegetation coverage of the j-th vegetation on the i-th day; m represents vegetation species in the river basin; n represents the total number of days of the year.
The method also comprises the following steps:
the total amount of invalid green water resources in the area to be measured is obtained by the following formula:
wherein Wgreen u Indicating the total amount of regional ineffective green water resources; w (W) un,i The evaporation amount of the soil from the unused land on the i-th day is shown.
The present embodiment is described with reference to specific model application examples:
1. study area overview: taking Guangxi south-flow river basin as a case for explanation, the south-flow river flows through Yulin, qinzhou, north sea.
2. Basic data input:
data such as meteorological observation data, DEM data, land utilization data, soil type data, hydrology, evapotranspiration, reservoirs and the like required by model construction.
Weather observation data: the four stations of Lingshan, yulin, qinzhou and North sea are 1990-2013 for precipitation, air temperature, air speed, solar radiation and relative humidity day by day; precipitation data day by day in three rainfall stations 1990-2013 of Pubei, bobai and Hepu;
remote sensing data: the DEM adopts 90m grid data provided by the national academy of sciences remote sensing and digital earth research institute; land utilization data and soil type data are provided by resource environmental science and data centers;
actual measurement hydrologic data: the flow data of four stations 1990-2013 of Zhuanjiang, bobai, hejiang and Changle are used for checking runoff month by month;
measured evaporation data: the evaporation capacity data of the evaporation dish of the Zhuanjiang, bobai and Changle three stations 2000-2011 month by month are used for verifying the evaporation;
crop growth data: the rice and sugarcane leaf area index 2005-2007 test data in the Hepu irrigation area are used for simulating and checking the crop growth;
reservoir data: data such as dead reservoir capacity, xingli reservoir capacity, total reservoir capacity and the like of 18 large and medium reservoirs; the data of the flow rate of entering and exiting the large-scale reservoirs of 3 seats such as the small river reservoirs and the like month by month in 1990-2013;
3. parameter calibration and model verification:
selecting the correlation coefficient R of the current selection 2 And Nash-Sutcliffe efficiency coefficient E ns To evaluate the simulation adaptability. It is generally considered that R 2 Greater than 0.6, E ns The simulation accuracy of the model is satisfactory when the simulation accuracy is greater than 0.6.
Simulation of plant growth:
the main vegetation types in the south-flow river basin are woodland and crops. The evapotranspiration of the woodland almost belongs to effective green water, and the area ratio of the grassland to the shrubs is not high, so that the woodland, the grassland and the shrubs are not calibrated any more; the crop types with the largest proportion are double-cropping rice and sugarcanes, the sowing area of the double-cropping rice accounts for about 90% of the total sowing area of the crops, and the sowing area of the double-cropping rice accounts for more than 5% of the total sowing area of the crops. The leaf area index of the crops in the early growth stage is low, a large amount of ineffective evaporation is generated, and parameters are required to be considered for calibration; the area ratio of the crop types such as vegetables is very small and is not considered any more. The corrected crop parameters are mainly 6 parameters such as FRGRW1, FRGRW2, LAIMX1 and the like, and are corrected by daily step length so as to reduce simulation and observation residual errors of leaf area indexes of crops as much as possible, and the main parameter calibration results of rice and sugarcane are shown in Table 1. The data were from rice and sugarcane growth observation experiments in the Syringe-irrigation district 2005-2007.
TABLE 1
Referring to fig. 2, it can be seen from fig. 2 that the simulation value is better fitted to the measured value. The results of the leaf area index rating and verification of the main plants are shown in Table 2, the correlation coefficient R of the rating simulation value and the measured value 2 And Nash efficiency coefficient E ns Almost all are above 0.90, and it can be seen that the leaf area index simulation effect of rice and sugarcane is good, and the Nash efficiency coefficient E is verified ns Correlation coefficient R 2 And the values are all above 0.8, so that the required value is achieved.
TABLE 2
Vapor emission simulation:
6 parameters such as ESCO, EPCO, SOL _K of three stations of the Yulin, bobai and Hepu stations are selected for calibration, evaluation indexes of month evapotranspiration simulation results of the three stations are shown in a table 3, and from the table 3, the table 3 and the table 4, the month evapotranspiration simulation values and the actual measurement values are better in line fitting degree in the flow process. Correlation coefficient R of periodic moon runoff analog value and measured value 2 And Nash efficiency coefficient E ns Substantially above 0.70; nash efficiency coefficient E for each hydrologic station during verification period ns Correlation coefficient R 2 And basically, the values are all above 0.60, and all reach the required values. The site sensitivity parameter calibration results are shown in Table 4, in which r represents the current parameter value multiplied by (1+r) and v represents the parameter value replaced by the given value.
TABLE 3 Table 3
TABLE 4 Table 4
And (3) evaluating effective green water resources:
referring to Table 5, the total amount of green water resources in the Nanfu river basin is 87.7 hundred million m 3 Wherein the vegetation transpiration ratio is the highest (65.4 hundred million m) 3 ) Accounting for 75.9 percent of the total green water resource in the whole river basin. According to the effective green water resource dividing method, according to the green water resource quantity distribution of different divided land utilization types and the coverage characteristics of different vegetation types, the effective green water resource quantity of the south-flow river basin is calculated to be 77.5 hundred million m 3 Accounting for 90.1 percent of the total green water resource amount of the watershed, and the ineffective green water resource amount is 8.6 hundred million m 3 Accounting for 9.9 percent of the total green water resource amount of the watershed. In terms of structural composition, the effective green water resource amounts of the woodland, fruit woodland and wetland are 46.3 hundred million m respectively 3 1.8 hundred million m 3 And 1.6 hundred million m 3 96.1%, 90.8% and 100% of the total amount of the respective green water resources; the effective green water resource amount of cultivated land is 22.4 hundred million m 3 Accounting for 79.7 percent of the total amount of green water resources in cultivated land; the effective green water resource amount of the grasslands is 3.3 hundred million m 3 Accounting for 86.1 percent of the total green water resources of the shrub forest land.
TABLE 5
Referring to fig. 5, in the spatial distribution, effective green water resources are mainly distributed in mountain areas and estuary plain areas. The ineffective green water resources are mainly distributed on a Yulin basin at the upstream of a south-flow river basin, a doctor basin at the midstream and a river mouth plain zone at the downstream, wherein the cultivated land is concentrated, and the exposed surface soil area of the early stage of crop growth is larger, so that more ineffective green water is generated. In contrast, the ineffective green water in mountainous areas is small, because the forest lands are concentrated, the vegetation coverage is large, and the green water utilization efficiency is high. Thus, the effective green water quantity can be effectively improved by increasing the forest land area. The invalid green water in the farmland of the south-flow river basin is bigger, a series of measures such as intertillage, straw coverage, plastic film coverage and the like are recommended to be adopted to reduce direct illumination, reduce water evaporation on soil surfaces and improve the utilization efficiency of farmland green water resources.
Example two
Fig. 6 is a schematic block diagram illustrating an apparatus for evaluating an effective green water resource based on a dynamic crop coefficient according to an exemplary embodiment, referring to fig. 6, there is provided an apparatus for evaluating an effective green water resource based on a dynamic crop coefficient, including:
the model construction module 101 performs division and extraction on the region to be detected to obtain drainage basin information, and divides the hydrological response unit according to the drainage basin information; constructing a SWAT model according to the hydrological response unit, the weather data, the reservoir data and the farmland management data which are acquired in advance;
the optimizing module 102 is configured to construct a mathematical relationship expression of potential transpiration of vegetation according to the pre-acquired crop information, soil information and meteorological information, and optimize a vegetation transpiration calculation formula in the SWAT model according to the expression so as to realize simulation of dynamic crop coefficients;
the parameter calibration module 103 is configured to evaluate an evaporation result and a crop leaf area index simulation result obtained by running the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data that are obtained in advance, and calibrate crop growth parameters and evaporation parameters in the SWAT model according to the evaluation result;
a judging module 104, configured to judge evaporation effectiveness according to a land cover type and a dynamic coverage calculation method;
and the result generation module 105 is used for simulating vegetation coverage day by utilizing the SWAT model according to the judging result, and obtaining the total amount of effective green water resources in the to-be-detected area according to the simulation result.
It can be understood that, according to the technical scheme provided by the embodiment, the SWAT model of the region to be detected can be constructed through the model construction module 101; constructing a mathematical relation expression of potential transpiration of vegetation through an optimization module 102, and introducing dynamic crop coefficients into the SWAT model; evaluating a result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance by a parameter calibration module 103, and calibrating crop growth parameters and evapotranspiration parameters; performing evaporation effectiveness judgment according to the land cover type and the dynamic coverage calculation method through a judgment module 104; and carrying out daily simulation through the result generation module 105 to obtain the total amount of effective green water resources in the region to be tested. According to the technical scheme provided by the embodiment, dynamic crop coefficients are introduced, so that the characteristics of specific plant types are fully considered in the model in the evapotranspiration simulation process, the actual evapotranspiration and leaf area index change conditions of different vegetation types in respective growth periods are accurately reflected, and the accuracy of green water simulation and crop growth process simulation is improved; on the basis of improving the simulation accuracy of the crop growth process, a dynamic coverage calculation method is introduced, so that the accuracy of the effective green water resource evaluation is improved, the water resource evaluation caliber is widened, and more effective guidance is provided for more effective utilization and scientific management of water resources.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The method for evaluating the effective green water resource based on the dynamic crop coefficient is characterized by comprising the following steps of:
dividing and extracting the region to be detected to obtain drainage basin information, and dividing a hydrological response unit according to the drainage basin information;
constructing a SWAT model according to the hydrological response unit, the weather data, the reservoir data and the farmland management data which are acquired in advance;
constructing a mathematical relation expression of potential transpiration of vegetation according to the pre-acquired crop information, soil information and meteorological information, and optimizing a vegetation transpiration calculation formula in the SWAT model according to the expression to realize simulation of dynamic crop coefficients;
evaluating the evapotranspiration result and the crop leaf area index simulation result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating crop growth parameters and evapotranspiration parameters in the SWAT model according to the evaluation result;
judging the evaporation effectiveness according to the land cover type and the dynamic coverage calculation method;
and according to the judging result, simulating vegetation coverage day by using the SWAT model, and obtaining the total amount of effective green water resources in the to-be-detected area according to the simulation result.
2. The method of claim 1, wherein the dividing and extracting the to-be-detected area to obtain the drainage basin information, and obtaining the hydrological response unit according to the drainage basin information, includes:
importing grid DEM data of the region to be detected into a geographic information system platform to obtain natural sub-river basin division data and river network water system data as river basin information;
and importing the river basin information into an initial SWAT model, and dividing the hydrological response unit according to the land utilization type, the soil type and the gradient type.
3. The method of claim 1, wherein constructing a mathematical relationship expression of potential transpiration of vegetation based on pre-acquired crop information, soil information, and weather information, comprises:
the mathematical relational expression of the potential transpiration of vegetation is as follows:
wherein EP represents the potential transpiration of vegetation; ET (electric T) 0 Indicating a reference vapor deposition amount; k (K) cini 、K cmid 、K cend Crop coefficients of vegetation in an initial growth period, a rapid growth period and a late growth period are respectively represented; gluc represents the plant heat accumulation unit percentage; fr gw1 And fr gw2 Respectively representing the heat accumulation ratio corresponding to the first point and the second point on the optimal leaf area index curve.
4. The method of claim 1, wherein evaluating the results of the transpiration and the simulation of the crop leaf area index from the pre-acquired measured evaporation data and leaf area index monitoring data from running the SWAT model comprises:
and selecting a correlation coefficient and a Nash-Sutcliffe efficiency coefficient to evaluate and operate the evaporation result and the crop leaf area index simulation result obtained by the SWAT model based on the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance.
5. The method of claim 4, wherein the step of determining the position of the first electrode is performed,
a rated crop growth parameter comprising: the method comprises the steps of determining a temperature accumulation ratio corresponding to a first point on an optimal leaf area index curve, determining a temperature accumulation ratio corresponding to a second point on the optimal leaf area index curve, determining a leaf area ratio corresponding to the first point on the optimal leaf area index curve, determining a leaf area ratio corresponding to the second point on the optimal leaf area index curve, and determining a potential maximum leaf area index and a temperature accumulation ratio corresponding to the beginning of decay of the leaf area index;
the rated evapotranspiration parameters comprise: soil evaporation compensation factors, plant absorption compensation factors, shallow groundwater re-evaporation coefficients, shallow aquifers're-evaporation' or penetration to a threshold depth of a deep aquifer, soil layer effective water capacity and vegetation retention capacity.
6. The method of claim 1, wherein the determining the evaporation effectiveness according to the land cover type and the dynamic coverage calculation method comprises:
the water which participates in vegetation transpiration is judged to be effective green water resources by intercepting and evaporating the canopy, intercepting and evaporating the building in the construction site, evaporating the swamps or water areas and diving and evaporating;
calculating the vegetation coverage of the forestation land and the non-high-density grassland by using a dynamic coverage calculation method;
soil evaporation between vegetation is calculated from the coverage of the forestation, non-high density grassland vegetation.
7. The method of claim 6, wherein the step of providing the first layer comprises,
the formula of the dynamic coverage calculating method is as follows:
a i ＝LAI i /LAI mx
wherein a is i Vegetation coverage representing the day Ji Di i of vegetation growth; LAI (LAI) i Leaf area index representing the days Ji Di i of vegetation growth; LAI (LAI) mx Indicating maximum leaf area in vegetation growth periodA number.
8. The method of claim 7, wherein the step of performing a daily simulation using the SWAT model, and obtaining the total amount of the effective green water resources in the area to be tested according to the simulation result comprises:
the total amount of the effective green water resources in the area to be measured is obtained by the following formula:
wherein Wgreen a Representing the total amount of effective green water resources in the area; w (W) can,i Represents the trapped evaporation amount on the i day; wwet i Represents the evaporation capacity of the wetland on the i th day; wwt i Indicating the evaporation capacity of the water area on the ith day; wgw i Represents the diving evaporation amount on the i th day; wep ij Representing the transpiration amount of vegetation on the ith day of the jth vegetation; wes ij The soil evaporation capacity of the j-th vegetation on the i-th day is represented; w (W) un,i Indicating the evaporation amount of the soil of the unused land on the i-th day; a, a ij Representing vegetation coverage of the j-th vegetation on the i-th day; m represents vegetation species in the river basin; n represents the total number of days of the year.
9. The method as recited in claim 8, further comprising:
the total amount of invalid green water resources in the area to be measured is obtained by the following formula:
wherein Wgreen u Indicating the total amount of regional ineffective green water resources; w (W) un,i The evaporation amount of the soil from the unused land on the i-th day is shown.
10. An effective green water resource evaluation device based on dynamic crop coefficients is characterized by comprising:
the model construction module is used for carrying out division and extraction on the region to be detected to obtain drainage basin information, and dividing the hydrological response unit according to the drainage basin information; constructing a SWAT model according to the hydrological response unit, the weather data, the reservoir data and the farmland management data which are acquired in advance;
the optimizing module is used for constructing a mathematical relation expression of potential transpiration of vegetation according to the pre-acquired crop information, soil information and meteorological information, optimizing a vegetation transpiration calculation formula in the SWAT model according to the expression, and realizing simulation of dynamic crop coefficients;
the parameter calibration module is used for evaluating the evapotranspiration result and the crop leaf area index simulation result obtained by operating the SWAT model according to the actually measured evaporation data and the leaf area index monitoring data which are obtained in advance, and calibrating the crop growth parameters and the evapotranspiration parameters in the SWAT model according to the evaluation result;
the judging module is used for judging the evaporation effectiveness according to the land cover type and the dynamic coverage calculation method;
and the result generation module is used for simulating vegetation coverage day by utilizing the SWAT model according to the judging result, and obtaining the total amount of effective green water resources in the to-be-detected area according to the simulation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310517442.2A CN116579880B (en) | 2023-05-06 | 2023-05-06 | Effective green water resource evaluation method and device based on dynamic crop coefficients |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310517442.2A CN116579880B (en) | 2023-05-06 | 2023-05-06 | Effective green water resource evaluation method and device based on dynamic crop coefficients |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116579880A true CN116579880A (en) | 2023-08-11 |
| CN116579880B CN116579880B (en) | 2024-05-03 |
Family
ID=87543766
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310517442.2A Active CN116579880B (en) | 2023-05-06 | 2023-05-06 | Effective green water resource evaluation method and device based on dynamic crop coefficients |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116579880B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102349371A (en) * | 2011-08-07 | 2012-02-15 | 吉林省农业科学院 | Method for quickly desalting, dealkalizing and ploughing soda saline-alkali paddy field |
| CN112765800A (en) * | 2021-01-12 | 2021-05-07 | 长江水利委员会长江科学院 | Design method of distributed water resource configuration model |
| CN114578022A (en) * | 2022-02-18 | 2022-06-03 | 中国水利水电科学研究院 | Plant water utilization efficiency evaluation method based on evapotranspiration component |
| CN115455707A (en) * | 2022-09-19 | 2022-12-09 | 中山大学 | Method for analyzing influence of drainage basin water resource engineering on meteorological-hydrological drought |
-
2023
- 2023-05-06 CN CN202310517442.2A patent/CN116579880B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102349371A (en) * | 2011-08-07 | 2012-02-15 | 吉林省农业科学院 | Method for quickly desalting, dealkalizing and ploughing soda saline-alkali paddy field |
| CN112765800A (en) * | 2021-01-12 | 2021-05-07 | 长江水利委员会长江科学院 | Design method of distributed water resource configuration model |
| CN114578022A (en) * | 2022-02-18 | 2022-06-03 | 中国水利水电科学研究院 | Plant water utilization efficiency evaluation method based on evapotranspiration component |
| CN115455707A (en) * | 2022-09-19 | 2022-12-09 | 中山大学 | Method for analyzing influence of drainage basin water resource engineering on meteorological-hydrological drought |
Non-Patent Citations (1)
| Title |
|---|
| 杨明智: ""基于二元水循环的水量-水质-水效联合调控模型开发与应用"", 《水利雪豹》, vol. 51, no. 12, pages 1473 - 1484 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116579880B (en) | 2024-05-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Evaluating the effects of limited irrigation on crop water productivity and reducing deep groundwater exploitation in the North China Plain using an agro-hydrological model: I. Parameter sensitivity analysis, calibration and model validation | |
| Karlsson et al. | Combined effects of climate models, hydrological model structures and land use scenarios on hydrological impacts of climate change | |
| Immerzeel et al. | Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment | |
| Kumagai et al. | Annual water balance and seasonality of evapotranspiration in a Bornean tropical rainforest | |
| Adeboye et al. | Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. merr) in Ile-Ife, Nigeria | |
| Dzikiti et al. | Estimating the water requirements of high yielding and young apple orchards in the winter rainfall areas of South Africa using a dual source evapotranspiration model | |
| Wang et al. | The characteristics of evapotranspiration and crop coefficients of an irrigated vineyard in arid Northwest China | |
| Miao et al. | Spatio-temporal variability of streamflow in the Yellow River: possible causes and implications | |
| Guo et al. | Quantifying water and energy budgets and the impacts of climatic and human factors in the Haihe River Basin, China: 1. Model and validation | |
| Mehta et al. | Simulated impacts of three decadal climate variability phenomena on dryland corn and wheat yields in the Missouri River Basin | |
| Li et al. | Modelling evapotranspiration in a Central Asian desert ecosystem | |
| Wang et al. | Simulated water balance of forest and farmland in the hill and gully region of the Loess Plateau in China | |
| Van Dam et al. | Water productivity of irrigated crops in Sirsa district, India | |
| Fontenot | An evaluation of reference evapotranspiration models in Louisiana | |
| CN104933626A (en) | River basin agricultural non-point source pollutant channel reduction coefficient calculation method | |
| Guan et al. | The relationship between sap flow of intercropped young poplar trees (Populus× euramericana cv. N3016) and environmental factors in a semiarid region of northeastern China | |
| Wei et al. | Role of reservoir regulation and groundwater feedback in a simulated ground‐soil‐vegetation continuum: A long‐term regional scale analysis | |
| Hublart et al. | Reliability of a conceptual hydrological model in a semi-arid Andean catchment facing water-use changes | |
| Zhang et al. | Agricultural drought characteristics in a typical plain region considering irrigation, crop growth, and water demand impacts | |
| Quan et al. | Future climate change impacts on mulched maize production in an arid irrigation area | |
| CN116579880B (en) | Effective green water resource evaluation method and device based on dynamic crop coefficients | |
| Derib | Balancing water availability and water demand in the Blue Nile: A case study of Gumara watershed in Ethiopia | |
| Wang et al. | Contribution of plant traits to the explanation of temporal variations in carbon and water fluxes in semiarid grassland patches | |
| CN114970355A (en) | Method for simulating Xinjiang poplar transpiration water consumption based on cross validation and neural network | |
| Pryor et al. | Integrating climate change, hydrology, and water footprint to measure water scarcity in Lesotho, Africa |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |